from pylab import *
from PIL import Image
# import pickle

# If you have PCV installed, these imports should work
# from PCV.geometry import homography, camera
# from PCV.localdescriptors import sift
import homography,camera,sift
"""
This is the augmented reality and pose estimation cube example from Section 4.3.
"""


def cube_points(c, wid):
    """ Creates a list of points for plotting
        a cube with plot. (the first 5 points are
        the bottom square, some sides repeated). """
    p = []
    # bottom
    p.append([c[0]-wid, c[1]-wid, c[2]-wid])
    p.append([c[0]-wid, c[1]+wid, c[2]-wid])
    p.append([c[0]+wid, c[1]+wid, c[2]-wid])
    p.append([c[0]+wid, c[1]-wid, c[2]-wid])
    p.append([c[0]-wid, c[1]-wid, c[2]-wid]) #same as first to close plot
    
    # top
    p.append([c[0]-wid, c[1]-wid, c[2]+wid])
    p.append([c[0]-wid, c[1]+wid, c[2]+wid])
    p.append([c[0]+wid, c[1]+wid, c[2]+wid])
    p.append([c[0]+wid, c[1]-wid, c[2]+wid])
    p.append([c[0]-wid, c[1]-wid, c[2]+wid]) #same as first to close plot
    
    # vertical sides
    p.append([c[0]-wid, c[1]-wid, c[2]+wid])
    p.append([c[0]-wid, c[1]+wid, c[2]+wid])
    p.append([c[0]-wid, c[1]+wid, c[2]-wid])
    p.append([c[0]+wid, c[1]+wid, c[2]-wid])
    p.append([c[0]+wid, c[1]+wid, c[2]+wid])
    p.append([c[0]+wid, c[1]-wid, c[2]+wid])
    p.append([c[0]+wid, c[1]-wid, c[2]-wid])
    
    return array(p).T


def my_calibration(sz):
    """
    Calibration function for the camera (iPhone4) used in this example.
    """
    row, col = sz
    fx = 2555*col/2592
    fy = 2586*row/1936
    K = diag([fx, fy, 1])
    K[0, 2] = 0.5*col
    K[1, 2] = 0.5*row
    return K



# compute features
sift.process_image('../data/book_frontal.JPG', 'im0.sift')
l0, d0 = sift.read_features_from_file('im0.sift')

sift.process_image('../data/book_perspective.JPG', 'im1.sift')
l1, d1 = sift.read_features_from_file('im1.sift')


# match features and estimate homography
matches = sift.match_twosided(d0, d1)
ndx = matches.nonzero()[0]
fp = homography.make_homog(l0[ndx, :2].T)
ndx2 = [int(matches[i]) for i in ndx]
tp = homography.make_homog(l1[ndx2, :2].T)

model = homography.RansacModel()
H, inliers = homography.H_from_ransac(fp, tp, model)

# camera calibration
K = my_calibration((747, 1000))

# 3D points at plane z=0 with sides of length 0.2
box = cube_points([0, 0, 0.1], 0.1)

# project bottom square in first image
cam1 = camera.Camera(hstack((K, dot(K, array([[0], [0], [-1]])))))
# first points are the bottom square
box_cam1 = cam1.project(homography.make_homog(box[:, :5]))


# use H to transfer points to the second image
box_trans = homography.normalize(dot(H,box_cam1))

# compute second camera matrix from cam1 and H
cam2 = camera.Camera(dot(H, cam1.P))
A = dot(linalg.inv(K), cam2.P[:, :3])
A = array([A[:, 0], A[:, 1], cross(A[:, 0], A[:, 1])]).T
cam2.P[:, :3] = dot(K, A)

# project with the second camera
box_cam2 = cam2.project(homography.make_homog(box))

# 测试:将点投影在 z=0 上，应该能够得到相同的点
point = array([1,1,0,1]).T
print(homography.normalize(dot(dot(H,cam1.P),point)) ) 
print(cam2.project(point)) 

# Rt = dot(linalg.inv(K), cam2.P)
import pickle
with open('../data/ar_camera.pkl','wb') as f: 
    pickle.dump(K,f,0) 
    pickle.dump(dot(linalg.inv(K),cam2.P),f)

print("ar_camera.pkl is generated!")

# plotting
im0 = array(Image.open('book_frontal.JPG'))
im1 = array(Image.open('book_perspective.JPG'))

figure()
subplot(221)
imshow(im0)
plot(box_cam1[0, :], box_cam1[1, :], linewidth=3)
title('2D projection of bottom square')
axis('off')


subplot(222)
imshow(im1)
plot(box_trans[0, :], box_trans[1, :], linewidth=3)
title('2D projection transfered with H')
axis('off')

subplot(223)
imshow(im1)
plot(box_cam2[0, :], box_cam2[1, :], linewidth=3)
title('3D points projected in second image')
axis('off')

show()

